GIS has proven its pro ciency in many elds but still require some more ob- servations. The spatial interpolation which has seen widespread used in diverse domains with multiple applicability. Currently, some commercial GIS or an- alytics program packages now provide spatial interpolation technique includ- ing inverse distance weighting (IDW), kriging, triangulated irregular networks (TIN) interpolation, kernel density interpolation (KDE). Further, the two spa- tial autocorrelation techniques i.e., also known as LISA indicators that com- prised of Moran's I and Getis-Ord G itechniques. Amalgamation of spatial interpolation and spatial autocorrelation are being shown in this paper. For the implementation, related enabling technologies are summarized. Further, this pa- per entailed the literature survey of multiple papers comprising the associated works carried out in the domain of spatial interpolation and spatial autocorre- lation areas of GIS. The proposed framework is a generalized framework with four tiers to realize geospatial systems. This paper scrutinized geospatial crime dataset for understanding the proposed framework realistically. Numerous re- sults are produced to prove the eciency of the proposed model. The paper entailed weighted-overlay analysis, symbological heatmap analysis, choropleth analysis, kernel density interpolation, and Getis-Ord G ispatial autocorrelation analysis implemented to practically realize the essence of geospatial crime an- Corresponding Author Email address: supriyapanigrahy1996.sp@gmail.com (Supriya Panigrahy ) Preprint submitted to Journal of L ATEX Templates May 4, 2021 alytics. Finally, future research directions are recapitulated, where the task of interpolation and autocorrelation might be merged with techniques of arti cial intelligence, machine learning, and cloud computing paradigm, serverless com- puting framework and many such in order to enhance the proposed structure with lessened restrictions. Keywords: Geographic Information Systems (GIS), Weighted-Overlay, Choropleth Analysis, Kernel Density interpolation, Getis-Ord G i Autocorrelation. 1. Introduction 1 With the use of GIS (geographic information system), we can open up our 2 own custom maps on our personal computers, or add new data to existing maps, 3 each of which would give us the freedom to display several di erent ways of 4 seeing or create additional map features are just and important as critical as data 5 storage and maintenance, for the proper expansion of the amount of geographic 6 information and thus making it easier to use (Geographic data Systems). It 7 might be very recent, coming into being during the 1970s, but GIS has expanded 8 tremendously over the years will be available to only to certain businesses and 9 colleges who can a ord expensive equipment to provide computerized geographic 10 information systems (GIS). Today, almost anyone who has a personal device 11 is using geo-coding tools, whether it's a desktop or a home computer. More 12 recently, GIS applications have been made simpler to use. There's no need for 13 a huge amounts of training; in fact, it is comparatively simple even for a novice 14 and beginner users. GIS is no longer just software: managing and processing 15 geographic information; it may also include managing and handling any and/all 16 geographic information. // 17 Geographic Information Systems (GIS) has emerged as a prime technology 18 is diverse domains such as disaster management, susceptibility assessment, epi- 19 demiological analysis and many such. In a cartographic viewpoint, GIS plays 20 a key role in designing maps and corresponding vector les [1, 2, 3]. The pro- 21 2 ciency of spatial interpolation techniques as well as spatial autocorrelation 22 techniques in identifying heatmaps, hotspots, and coldspots has led to immense 23 rage these days [4, 5]. Interpolation is a widely used method to classify data 24 points in order to identify dense and sparse regions based on pseudo-color inten- 25 sities [6]. Further, spatial autocorrelation techniques are used to identify risk 26 zones (hotspots and coldspots) [7, 8]. 27 28 As is critical in designing and making charts, it also has a crucial tasks when 29 it comes to making vector les. To make an interpolation, we have to presume 30 that the information is available, or as long as there is no available information 31 we can use some mathematical tool to make an approximation. A often used 32 strategy in expanding strings is to draw on distinct data to provide a more con- 33 tinuous form. And we switch from a vector to a raster, for interpolation. And, 34 thus, input data can be in the form of a point, line, or it can be in the form of a 35 waveforms. If we have identi ed these speci c shapes on the computer graphics 36 programmers have open, it's simple to take those contour points and translate 37 them to continuous coordinates. We will have to expand on the de nition of 38 contour because our point representation, whether discrete or continuous, is re- 39 quired. 40 41 In GIS, spatial interpolation can be used to measure any surface property at 42 a speci ed point, to create contours for viewing data graphically, and it's widely 43 used as a tool in spatial decision-making processes including terrain analysis, hy- 44 drology, resource prospecting, hydrocarbon discovery, and crime analysis. Until 45 going through various interpolation strategies, it's important to go through the 46 variations in surface representation methods; each representation is useful in 47 a particular circumstance. Furthermore, A grid representation of a surface is 48 called a functional surface since it holds only a single z value rather than mul- 49 tiple z values for every given x, y position, and the surface values are constant 50 rather than discrete. 51 52 3 The global expanders de ne a single feature which is applied to the entire 53 eld, for example, for trend surface illustration and example, for drawing an 54 ascending or descending line trend. For example, the use of local interpolator's 55 sample-weighted vectors is done repeatedly on a smaller subset of the total num- 56 ber of points, for instance, IDW. Exact interpolators preserve the input data 57 while approximates allow for approximation. The more random approaches use 58 the principle of non- t surfaces (in several ways close to linear regression, in- 59 cluding a best- t plane) and the less random ones do not use probability theory. 60 Other expanders allow for faults (such as those), and to appear inside the data, 61 whereas the Smooth interpolators provide a smooth surface. Mathematical and 62 numerical (and geometric) interpolation is two kinds of interpolation, one of 63 which is linear and the other is non-linear. More importantly, interpolation 64 strategies can be divided into the following ve groups: 65 66 •Kriging is a geostatistical method for computing the magnitude of a ran- 67 dom eld (e.e. elevation, z) at an unknown position by interpolating 68 between observed values. Daniel Krige's Master's thesis helped re ne and 69 improve the technique of interpolation and extrapolation, and was used 70 by the French mathematician Georges Matheron in the analysis of their 71 ideas. Global expansion is gradual, smooth, or sudden, or local; Kriging 72 is stochastic, exact, or precise, or stochastic. 73 74 •Shortened inverse distance Point-weighted interpolation: This type of 75 weighted average includes the weighting e ect on the interpolation method: 76 calculates cell values by combining the values in the neighborhood of the 77 samples. To and subject cell value, an equal distance from the closer a 78 point has in the cell, the formula, the more power or weight it has in the 79 averaging. 80 81 •Spline Interpolation: This technique uses a mathematical function that 82 4 avoids the e ect of total curvature, and ensures that the input points 83 lie on a smooth surface, allowing the calculated values to ow precisely 84 through the points. 85 86 •Triangulated Irregular Network: The TIN (3D geodatabase) is a data 87 structure that is used in GIS to illustrate a surface, meaning it's a dis- 88 torted in shape and uses a geometric, equal-area Cartesian grid. or A, 89 TINs can be produced by either by conversion of a functional surface data 90 or by the application of a pattern recognition technique to another func- 91 tional surface. 92 93 •Kernel Density Interpolation: Instead of using a parametric function for 94 calculation of likelihood density and using it, K-nearest is an alternative 95 that doesn't create a function for data smoothing. Spatial autocorrelation 96 tests the association with its constituent values across the magnitude of 97 the region under consideration. 98 99 The result of spatial autocorrelation can be either helpful or detrimental. 100 While people are seeing the same experiences in the same locations, they ap- 101 pear to be in agreement; yet when circumstances are di erent, they tend to 102 disagree. 103 104 •Moran's I: I believe that the most frequently employed strategy in spatial 105 autocorrelation is I is ordinary world autocor (i.e. a unique combina- 106 tions of events, each world, and native spatial autocor), Individuals of the 107 American Indian descent, Native Americans may consider their quality 108 of life to be good, may have higher cultural and nancial standards than 109 other people of the United States. Correlation coecients in Moran arises 110 from the whole universe of contrasts, which results in variances which are 111 5 greater than those of the interaction between the 2 conjoint variables is 112 measured in terms of their respective utility. 113 114 •Getis-Ord: In general Getis-Ord G can have a large value, which indicates 115 a correlation for prime averages of the strength, while an occasional value 116 indicates a correlation for low intensity values. It is important to provide 117 spatial autocorrelation. 118 119 Since it's often interpreted as suggesting that there's anything of concern 120 inside the distribution of map values that necessitates more analysis in order to 121 comprehend the reasons for the discovered spatial variance, Furthermore, the 122 existence of spatial autocorrelation means data redundancy, which has impor- 123 tant repercussions for spatial knowledge processing methodology. 124 125 The rage of GIS technology in the domain of computer applications has led to 126 immense applicability in sectors of disaster management, epidemiological anal- 127 ysis, susceptibility mapping and many such. This paper entails a novel strategy 128 to converge spatial interpolation techniques with spatial autocorrelation meth- 129 ods in order to perform proper risk as well as zonal analysis. The pro ciency of 130 GIS technology has been recently proved in risk and zonal analysis of various 131 prevailing epidemics as well as disasters and crime analysis. But, some lacu- 132 nae still persists in providing predictive competency to the model. This paper 133 tries to overcome such hurdles through a proposed framework on convergence of 134 spatial interpolation and spatial autocorrelation. The outcome of the proposed 135 framework is to provide appropriate risk analysis along with zonal analysis by 136 identi cation of hotspots as well as coldspots. 137 138 This is a realistic representation of the system with geospatial analysis, or 139 weighted overlay, weighted factors present the main challenge of measuring crime 140 locations in the United States, particularly in the initial stage. In a region- 141 6 speci c way, a mapped weighted average shows crime per capita. To distinguish 142 crime hotspots based on their severity, a symbological graded heat map was cre- 143 ated where crime rate varies geographically few estimates are given. In addition, 144 a choropleth map, which is simply a collection of evenly-spaced points joined 145 to the vector dataset is shown. An obvious goal of geospatial crime analytics is 146 to use kernel density to calculate the interpolation. The radius is set to 5 units 147 and an interpolated map is created. Additional, Getis-Ord G i, a local measure 148 of spatial interaction, denotes spatial autocorrelation Spatially auto-correlated 149 can be classi ed as hotspots and coldspots based on assigned zone scores. The 150 ndings reveal a well-grounded study of crime data on law enforcement proce- 151 dures and other policies implemented in the U.S. in di erent areas. Often, the 152 ndings may be charted on a zone-based study of U.S. criminal statistics. 153 2. Related Works 154 2.1. Spatial Interpolation and Autocorrelation in Crime Hotspot Mapping 155 Ansari and Kale [9] discussed that Crime detection has been an important 156 feature of GIS, where analysts utilized the geographical and temporal aspects of 157 crime data for analysis and forecasting utilizing the Hotspot analysis method. 158 A hotspot is a region where there is a high incidence of crime. There are many 159 types of Hotspot detection techniques, including Spatial Analysis, Interpolation, 160 and Spatial Autocorrelation, that can be used to locate a crime hotspot. The 161 methods of Kernel Density Estimation, Inverse Distance Weighted, and Getis- 162 OrdG ifrom each class are explored in this article. These techniques were used 163 in Aurangabad, Maharashtra, India, to identify hotspots for crime. Recent 164 crimes indicate that the requirement for a massive expansion of facilities to de- 165 tain criminals. Cops have an obligation to stay in a step ahead of the suspects, 166 who are perpetually re-veining the eld with police by trying to keep ahead of 167 the lawbreakers, who always want to surpass their e orts. This paper proved 168 that KDE (Kernel Density Estimation) and IDW (Inverse Distance Weighting) 169 for spatial interpolation and Getis-Ord G ifor spatial autocorrelation are more 170 7 precise than other techniques. 171 172 Kennao et al. [10] summed up that Policing authorities aren't good at fore- 173 casting where speci c crimes will arise in the future; As a result, geography 174 plays an important part in law enforcement and criminal justice. The author 175 signi cantly highlighted the most identi ed crime in the police stations of Alla- 176 habad city via a thematic chart that police departments may use in this analysis. 177 Author and the group decided by an interpolation process that a crime hotspot 178 analysis chart would include information on o ences. The police force must 179 identify and describe sources of crimes for the information they want in order 180 to stay ahead of criminal behavior patterns. The interpolation process (IDW) 181 was used to create a hotspot chart to show the concentration of crime (both 182 crimes against life and crimes against property) in each police station. The 183 police department's crime problems necessitate the use of computerized crime 184 mapping for research. Crime analysis plays a crucial role in devising solutions 185 in formulating crime preventions strategies and crime problems. For the form of 186 crime investigated in this paper, the jurisdictions of Doomanganj Police Station 187 and Kernal Ganj Police Station need increased patrolling and successful steps. 188 189 Jakobi and podor [11] analysis aimed to determine whether human charac- 190 teristics such as gender or age have an impact on crime fear, suggesting, for 191 instances, that aged people are more fearful of things that young people are less 192 afraid of, or that women might experience fear in di erent places than men. 193 Using visual sketch maps and predictive GIS tools, this analysis analyses ap- 194 prehension of crime experience and social crime statistics in a spatial sense. 195 By analyzing the outcomes of selected big, medium, and small sized Hungarian 196 towns, the research aimed to assess causal reasons for fear of crime. There was a 197 web-application where people could identify locations on a chart where they felt 198 secure, allowing them to track crime information that was available. To make 199 these interactive maps comparable, GIS technology was used to process the re- 200 sults. Such items and others were mirrored by the grid-based model. According 201 8 to the ndings, there were papers of both coincidences and reverse associations 202 between crime rates and perceptions of dangerous locations, demonstrating the 203 role of location in crime fear study. Furthermore, the ndings revealed that 204 while the scale of the area or the gender of the respondent had no substantial 205 impact on total judgments of locations, the absolute amount of protected mark- 206 ings and the local number of recorded crime incidents can have an impact on 207 local outcomes. 208 209 Hu et al. [12] epitomized in the paper to track geographic distributions of 210 historical crime cases and forecast possible crime trends, various spatial analy- 211 sis approaches have been used. The rst approach counts crime cases and then 212 measures statistics based on regional areas including census units. The multi- 213 variate regression approach is then used to investigate the association between 214 crime rates and a variety of crime attraction and inhibition factors, including 215 social circumstances, community populations, land use types, cultural beliefs, 216 and drug misuse records. In hotspot policing, predictive hotspot analysis is im- 217 portant. Existing techniques, such as the often used kernel density estimation 218 (KDE), do not take the temporal component of crime into account. The article 219 suggested a spatio-temporal system for predictive hotspot mapping and assess- 220 ment, building on previous studies in related elds. In comparison to previous 221 work in this area, the proposed structure has four signi cant characteristics: 222 223 •A spatio-temporal kernel density estimation (STKDE) technique is used 224 to incorporate the temporal component into predictive hotspot mapping, 225 •a data-driven optimization technique called likelihood cross-validation is 226 used to choose the most appropriate bandwidths, 227 •a statistical signi cance test is used to exclude false positives in the density 228 estimates, and 229 •a brand new benchmark was commenced to project precision inventory . 230 9 231 Ristea et al. [13] have shown that society's level of real and suspected vi- 232 olence in metropolitan environments is critical and that of crime is crucial in 233 maintaining a healthy community. It turns out the, that fear of crime occurs 234 at a greater pace than recorded incidents of crime. Urban decay describes the 235 issue where a neighbor-hood declines due to poverty and related issues. Geo- 236 graphic Information Science (GISc) and the use of geospatial technologies have 237 greatly bene ted spatial crime detection and perceived (crime) protection anal- 238 ysis. This paper used an innovative analytical methodology to chart the usage 239 of new geospatial technology to investigate perceived community protection in 240 Baton Rouge, Louisiana, through the lenses of fear of crime and crime inter- 241 pretation. A survey, spatial video geonarrative (SVG) in the eld with research 242 subjects, and the extraction of moments of tension (MOS) from bio sensing 243 wristbands are among the mixed techniques used. The participants in this re- 244 search were 46 people who completed geonarratives and MOS identi cation. 245 Here is a selection of ten of these geonarratives, Each participant was driven 246 along a predetermined route in a car tted with audio recording and spatial 247 video while wearing Empatica E4 wristbands that measured three physiological 248 variables, all of which were connected by timestamp. The ndings reveal gender- 249 based disparities in participants emotions (positive or negative) and MOS in the 250 area. This mixed-methods approaches are promising for establishing links be- 251 tween real crime occurrences and neighborhood perceptions of crime in urban 252 communities. 253 254 Yang [14] asked for four spatial trends in violent crime to travel to be ana- 255 lyzed: 256 257 •Analyses of street violence and learning environment patterns across schools 258 since the safe passage program was established, 259 •creating a web-enabled mobile application to allow researchers and resi- 260 10 dents to collect and share GIS data on the situation, 261 •enhancing a research network of shared information about it, and using 262 it to drive robotic vehicles to patrol these locations (to search for speci c 263 patterns and locations), 264 •undertaking a process to gather and analyze geo-referenced-maps of areas 265 that will provide them with these ndings. 266 Via this study the author discovered that, while the amount of secure routes 267 have risen in these regions, hotspot rates for gun violence have also become 268 worse over the years. GIS has then serves as a great foundation for community- 269 based study initiatives. In his paper, Yang looked at gun-related shootings in 270 each of the four communities. To that purpose, ArcGIS Online, a cloud-based 271 visualization tool, was used to build maps for gun-related crime counts. It's im- 272 portant to communicate scienti c ndings or geospatial data with the general 273 public because it allows them to engage in current studies in their communities. 274 As a result of this study, an interactive map was created that included neigh- 275 borhood services as well as the results of the spatial analysis and statistics. The 276 author illustrated Numeric data with region details is transformed into spatial 277 data with geographic coordinates using GIS-based charts. GIS is an excellent 278 forum for both urban populations and academics, since it o ers a geographic 279 overview of group partnerships and hotspots, along with local infrastructure 280 and crime statistics. 281 282 Ristea and Leitner [15] highlighted few research articles having devoted dis- 283 cussions to approaches that can address the question known police patrol route 284 problems, the two main ones being trac enforcement (the issue here) and 285 population monitoring (the issue being ignored). Crime prevention and law en- 286 forcement are also needed because the police would make sure that no criminals 287 are found, but can tend to crimes if they happen. They laid out the points, 288 from the street (or neighborhood) down to the city of Milwaukee, arguing that 289 if broken windows are not cleaned up, crime would increase (US). It is proposed 290 11 that further study of this hypothesis is required for many factors that include: 291 292 •violent broken window enforcement against marginalized populations; 293 •disparities in crime intensity, 294 •disturbance styles and their dominance in crime; and , 295 •the ethnic strati cation in neighborhoods. 296 This study explored the question of whether there is a relationship between 297 crime and disorder, as other studies in the authors have studied in the near 298 past, but has focused on the di erent details, and found it to be only partly 299 applicable. However, it seems that the result is to be restricted to public area 300 disturbance, with a more powerful bearing on the second category of o ences, 301 which correlates with less severe violence in the community as a result, rela- 302 tionships with the city should resolve problems with neighborhood incivility are 303 commonly practiced. The authors developed online and interactive maps,that 304 be used to survey a variety of the communities that include these medium and 305 small cities in Hungary, as well as Hungary's larger and smaller municipali- 306 ties. The aim of this analysis was to nd out why people perceive the safety 307 in lab studies as important. Overall, the results are only quite mixed because 308 of the similarities and disparities within studied metropolitan environments. It 309 appears to be the residents opinions about safety and how often criminal activ- 310 ities happenings are happening which shape their beliefs about the security of 311 a particular area. The cities and the scale do not seem to a ect the judgment 312 is insigni cant in relation to the population size; on the other hand, gender is 313 considered important for the total number of respondents Expanding on this, 314 the ndings show the signi cance of the local view in interpretation of protec- 315 tion testing. The author were studying the improvement in spatial precision for 316 predicting crime in the case of burglaries in Wuhan Houkao involves analyzing 317 an extension of related concepts. They put forward a more multidimensional 318 crime model, as the standard one only accounts for temporal and spatial aspects, 319 12 anisotropic di usion (AnisDM), since it contains external variables, which are 320 able to vary in location. 321 322 Mohammed and Baiee [16] explored that there are so many places where 323 crime must be researched and studied for someone to examine and analyze it 324 carefully in just one place. For the past few years, there has been an increased 325 social, socioeconomic, and person dysfunction, which is having a measurable 326 impact on crime rates. This has made the collection of information regarding 327 crime more challenging than it was used to be as a result of modern practices 328 and procedures being brought in to bear. Crime prevention can be aided by a 329 great advancements in our day, including GIS and can be a helpful in providing 330 alternative solutions to locate better solutions. The paper illustated an increase 331 in crime rates in developed countries is mostly attributable to the uneven distri- 332 bution of psychological and economic hardship. This study seeks to classify the 333 crime-probation areas and examine the historical crime data to ascertain where 334 criminal activity may concentrate during the next four years, with the goal of 335 gauging their tendency to create crime patterns and predicting them spatially. 336 Both of the author have put forward their e orts in Examination of crime data 337 using GIS enables law enforcement to recognize and monitor trends that al- 338 low for exibility in implementing spatial crime control strategies and makes 339 it simple to visualize crime clusters and move the crimes from one area to an- 340 other. The most helpful method of localizing the location of criminal incidents 341 for crime trend identi cation is through means of GIS; a viable approaches to 342 nding criminal hotspots include spatially expanding one's boundaries in order 343 to criminal pattern details and crime correlation; detection of potential patterns 344 is done by examining criminal incidents from a geographic perspective. Using 345 spatial correlation, the Getis-Ord G iresulted in the conclusion that the crime 346 data are used to plot statistically relevant crime hotspots to nd locations with 347 high and low concentrations as well as identi ed less dense areas of criminal 348 activity for increased detail with more e ectiveness using a GIS. This study, 349 with the help of the Grid network, was used to identify the danger locations 350 13 of violence like Shooting, Homicide, and Assault as well as crime hotspots in 351 the state of Baltimore, Maryland. The article explored that GIS is capable of 352 visualizing and temporal relations including violence, such as incidence, in ad- 353 dition to spatial features, is often capable of being used to forecast events based 354 on the method by which the areas are used. Ensconced with delivering un- 355 derstandable, real-life-world data in the decisions that enable multi-application 356 comprehension, even those inside Defense, and in disaster scenarios, the foun- 357 dation provides users with focused, practical applications. While dynamic or 358 expandable Hotspot and zone maps can also be represented using Getis-Ord G i359 these are generally considered dangerous. 360 361 Through this research Hashim et al. [17] attempted to identify high-risk 362 areas for opioid activity hotspots to avoid accidents and improve emergency 363 response times. When we use "hotspot" to describe a location with a higher 364 density of information and it occurs in the geographic system, we use the term 365 "geo". The rst phase in the analyst's search for problems associated with drug 366 distribution is locating these hotspots, when each area can or may be di erent 367 from others. Use of visualization and statistical analysis of illicit drug use pat- 368 terns can assist in locating the high concentration of users of illicit drugs in re- 369 gional areas. In this research authors emphasized the drug use is much stronger 370 and is a ecting the population's well-being. people have a habit of concluding 371 that approximately 133,684 to 1/4 (or 20,684 individuals) of the population in 372 Malaysia is addicted to drugs. Furthermore, the federal prison population of 373 56% consists of individuals who are serving sentences for drug-connected crimes. 374 This research is intended to classify the parts of Selor, Malaysia that are par- 375 ticularly problematic for opioid users. Using three separate statistical methods, 376 kernel density (KDE), Getis-Ord Gi, and IDW mapping, the researchers aimed 377 to illustrate the concentrations of opioid usage among the various addicts. The 378 hotspot algorithm calculates density-depth curves using the Gaussian kernel 379 density estimate (KDE), and Getis-Ord G iand IDW. The ndings indicated 380 that there are eight statistically important areas with high rates of opioid use 381 14 in the subdistricts (99% certain and p-value is less than 0.001). Although the 382 investigation of drug hotspots utilising GIS methods has not yet concluded, 383 the preliminary data already indicates that this is a very promising solution. 384 The present research has implemented multi-dimensional GIS mapping on the 385 hotspots for opioid use in an in order to elucidate a more complex geographic 386 picture of them. 387 388 Achu and Rose [18] explored GIS analysis tells, the current level of crime 389 , as well as its history and likely future trends. By using this study both the 390 author enlightened that the incidence of crime is rising in developed countries 391 owing to poverty and inequality. This research would look at the temporal and 392 spatial variation in crime in Thiruvanthapuram over the time span of 2010 to 393 2014 to learn more about crime occurrence. Computer aided Geographic infor- 394 mation tools like GIS, in addition to providing an increased level of detail for 395 law enforcement, have the potential to draw up maps that enable them to nd 396 areas of criminal activity and papering, geographic correlation, is applied to the 397 resulting hotspot patterns to determine the origin of crime hotspots, and inter- 398 polation is used to further pinpoint the location of criminal events among the 399 others. Spatial auto-correlation has been conducted before in order to discover 400 Getis-Ord G ispace anomalies and outlier phenomena previous to Getis-Ord 401 G idetecting trend analysis of spatial auto-correlation. Crime analysis utilises 402 four key components, which include venue, timing, rates, factors, and types, in 403 identifying areas where statistically important crimes are more likely to occur, 404 then makes use of IDW interpolation to provide a stronger visualisation. One 405 of the uses of this methods on the data from Thiruvanthapuram in Kerala is for 406 the areas of high crime prevalence including manslaughter, robbery, stealing, 407 and petty theft cases in that jurisdiction. 408 409 Rastogi et al. [19] signi ed the applications of GIS in crime visualization 410 and crime detection and investigation are indispensable response capabilities 411 are more commonly implemented using a combination of data from many or- 412 15 ganizations and outlets. Via an internet search of cases in police records, both 413 through past observation, investigators may identify the suspects that have been 414 recently been detained in certain areas and determine the crime's hot spots. The 415 group explored that due to both a spike in violence and the social problems, 416 there is a need for new ways to be developed to deal with this many measures 417 have been put in place to lower the amount of crimes against women in towns, 418 but many of these cities are also considered dangerous. The proposed study 419 seeks to consider the socio-economic issues contributing to an increase in the 420 risk of violence against women. Inverse-Weighted (IDW), Kriging, and Spline, 421 and even, in order to explore the relationship between socio-economic charac- 422 teristics and socio-emotional parameters on these maps more comprehensively. 423 This is the best interpolation method: by using the best selected interpolation 424 methodology, the thematic maps are generated to forecast the trend of criminal 425 activity. Once the proposed architecture has been built using GIS and big data 426 technologies, it can be implemented as a real-time decision-making method for 427 helping those in need. True evidence is used to test the study's methods in 428 the Jhunjhunu district of Rajasthan, India, and a case study is conducted to 429 analyze it. It is captured by the group of authors that there is an increasing 430 need for an atmosphere of safety in Indian cities. 431 432 Zahrane et al. [20] signi ed in studying the use of three distinct types of 433 hotspot analysis to de ne and classify RTA hot spots and measure their impact 434 on road trac congestion, in order to arrive at reliable metrics for the resolution 435 of congestion issues. We have three available approaches at our disposal: the 436 Density (KDE) approach, which was recently endorsed by the Getis-Ord G i, 437 and the recently introduced cost-based Getis-Ord G i, system (STA) approach, 438 and the Getis- Ord G i, procedure for calculating cost-related risk, as proposed 439 in an expanding, both of integrating social risks in the model. Until analyzing 440 the RTA data using any of these di erent approaches, they were rst prepared 441 in digital form in ArcGIS, and then imported into ESRI ArcSDE programme. 442 Then, the actual safety measures (those listed above) are evaluated in relation 443 16 to the places, the number, region, and value of the locations where the RTA 444 is installed to nd out the priority and cost/bene t of each measure has with 445 each resulting metric. This article identi ed that in order to examine and de- 446 termine the reason for these very long-term market uctuations, a micro-level 447 study might be needed. Thus, the methods presented above, especially in regard 448 to stopping quickly with RTA expansion, have great value, but should not be 449 pursued if an RTA expansion is detected. Immediately once they have identi ed 450 a probable hotspot, the ndings should be checked and subsequently, they can 451 map out all the possible dangers that will lead to further collisions, so they can 452 discover what will have the greatest impact. 453 454 Butt et al. [21] explored that many trac-related incidents, the issue of 455 present times, are the result of human error, rather than bad weather or haz- 456 ardous conditions. The author Butt and group manifested in knowing where 457 the worst locations for car accidents and taking preventative measures to ensure 458 they do not occur is vital for trac protection in the following year. The RTA 459 wanted to create a list of places with increased trac risk and then monitor 460 it over time in order to identify areas that were more prone to accidents for 461 ve years in Rawindi, in this case the GIS was utilized to research the extent 462 of the potential areas with greater danger of road mishaps. The method was 463 utilized for the aforementioned reason was the one established by Morans I and 464 Getis-Ord G imethod on the data gathered from the Punjab Emergency De- 465 partment. Most places marked for expansion and signi cant to the analysis 466 in terms of spatial clusters were concentrated in the northern and northeastern 467 portions of the city neighborhoods, which often included both industrial and res- 468 idential areas, although most hotspots representing an accident were found in 469 locations near colleges, airports and highways. This paper pronounced that an 470 Early warning system (or early detection system) using geographic information 471 systems can help to reduce the amount of resources needed to respond to a crisis. 472 473 17 2.2. Geo-spatial Operations For Crime Visualization 474 Dede et al. [22] conducted a research project with the researchers and com- 475 puter programmers in Bandung hopes of gaining di erent knowledge on crime 476 location and then exploring the possibilities of identifying danger distribution 477 by means of GIS. A number of statistical methods are used to quantitatively de- 478 termine the factor values and/ from that, under which of the above quantitative 479 tests, a set of deciles, the law is drawn to gure out the value distribution By 480 applying GIS program, a formula needs to calculate crime rate (or score) and 481 meaning (or weight) to various factors, such as the dispersion and population 482 density, Eastman would be able to obtain overlay results for Bandung. The au- 483 thor and group analyzed that any of the factors contributing to Bandung's crime 484 rates include history, public services, the availability of property, the amount 485 of roads, as well as the density of the community, along with issues concerning 486 the inequality of the poverty distribution using of GIS means that the location 487 of most criminal activity. This puts a crime-risk at the agglomeration in the 488 centre and western Bandung, because the region has been prioritized as a police 489 concern. 490 491 Lee et al. [23] the aim of the research was to understand the existing crimi- 492 nal population's habits and behavior patterns in order to allow new solutions to 493 be proposed; it commenced by studying and understanding the criminological 494 patterns that had already been established in other cities, later on conducted 495 some research in this area to determine where the root causes of crime can 496 be found; it developed methods to classify the current CCTV-based causes of 497 crime for solution; and at the same time, provided diagnostic recommendations. 498 Looking at the crime features, aquatic and stationary populations, as well as 499 crime locations, it was discovered that the oating population had the greatest 500 density of all in and of the places where it occurs. This paper explored that 501 the need for Korean people to feel secure and protected has increased, resulting 502 in the nation pursuing long-long-term strategies for constructing safe cities as 503 a matter of priority. In order to deter urban crime, it is essential to learn as 504 18 much as possible about the everyday life of urban citizens, as well as about 505 the locations and features of the city where they live. According to this pa- 506 per this result supports the idea that oating population data should be used 507 in lieu of actual population data, as the diculties in collecting such data has 508 previously been cited as a problem. One big arterial roadway expansion after 509 another and City A's new residential developments focused on them, resulting 510 in the rapid growth of a oating community along these arteries. these places, 511 as a hotspot for violence, are the crime areas in these cities being associated 512 with people who stay by the water or in otation's, as well as the frequent 513 place "anchor" points for criminals, which could be reason for installing CCTVs 514 and other crime-prevention tactics including increased police patrols Instead, 515 however, places prone to violence seem to concentrate further such where it is 516 most visible as a direct results of individual events, with most of the changes in 517 the people's everyday lives (i.e. over the course of time while they're working, 518 socializing, or shopping ). 519 520 Faisal and Shaker [24] turn inside out that socially speaking, urban environ- 521 mental quality can be described as a description of how urban, environmentally, 522 the climate, and in terms of socio-economic factors are doing. A few partial at- 523 tempts have been tried with a multi-temporal and multi-resolution data in the 524 past, as these datasets give a clear picture of both visual and understand land 525 cover (to) water coverage and vegetation patterns in urban areas which helps 526 identify further patterns and patterns of usage and develop more extensive de- 527 tail in the Urban EQ assessment does so, as well, being thus more bene cial 528 to sustainable development and urban planning. They signi ed into that the 529 United Nations has predicted that the world population would double in the 530 planet within the next forty years, and have an e ect on the atmosphere and 531 human well-being. The goal of this research was to use GIS and remote sensing 532 to analyze the UEQ with an in the case of Toronto, to gain a better under- 533 standing of the impact of various natural, urban, and socioeconomic conditions 534 on data-related trends. There was then a need to gather and monitor natural, 535 19 urban, and socioeconomic data, as well as geography information using remote 536 sensing, and GIS technology. To measure the e ects, they collected comparison 537 data and examined the UEQ relationship to see how that it a ects the quality 538 of the test's outcomes. More precisions and accuracies, at 71% and 65%, are 539 recorded by the GIS technique. This project would provide the municipality 540 with a generic metric to assist with preparing with regard to all educational, 541 environmental, and urban requirements where you might have a few other kinds 542 of results to monitor. Both the authors explored that via the use of two di erent 543 methods: 544 545 •GIS overlay (on a map) and, 546 •PCA, the results of this research aimed to evaluate the correlation of UEQ 547 with the study location in the city of Toronto, Canada. 548 Srivas and Khot [25] discussed a variety of tools and methods for multi- 549 dimensional database and visualizations that are available for complex data 550 sets. In this article they mainly covered the popular open source Quantum GIS 551 (QGIS) visualization tool which works well with several di erent visualization 552 functions, as well as complex data types and data models. They are mostly used 553 for obtaining several representations of records, unions and combination of num- 554 bers, but can also be used for data extraction and numerous other processes. A 555 bonus aspect that the consumer may take advantage of is the greater precision 556 of the software is Edit Spatial Information and exporting maps to and diagrams. 557 The software application of GIS is well suited for creating geospatial decision 558 support systems (DSS). The process of collecting a vast volume of spatial data 559 takes a lot of time and resources. Research has resulted in di erent techniques 560 for spatially enhanced research which are quicker and provide improved analysis 561 outcomes in lessening the amount of time spent on its completion. The Spatial 562 Data Management box o ers a wide range of resources that are made available 563 for generating, managing, and managing, and xing problems, as well as for 564 growth, design, management, and advancement, by using function datasets. 565 20 566 Je erson [26] made use of a mixed-methods methodology, taking into ac- 567 count work with the Predictive Analytics, as well as analytical documentation 568 pertaining to city-planning documents from the police, to examine the role of 569 predictive technology in co-prediction on complex problem-solving with the so- 570 cialized policing. Indeed, there is a rising number of geospatial intelligence 571 specialists and law enforcement agencies as well as well as criminal justice de- 572 partments that see GIS and predictive analytics playing a role in improving 573 police targeting to "to placing a focus on the arrest of criminals at particular 574 places rather than on "groups of persons or geographies. The author signi ed 575 with the added consideration of temporal factors, these Chicago researchers are 576 investigating the dynamics of department partnerships, including geographical 577 intelligence scientists and police experts who are presently collecting time-based 578 data and spatial information in GIS maps to determine where potential criminal 579 activity will arise. The author focused on important police-distant policing as 580 well as well as on societal spatial dynamics to learn more about the extent to 581 which predictive crime mapping has on shared data strategies. 582 583 Jayakrishnan and Shook [27] demonstrated how spatial simulation methods 584 can be used to extend spatiotemporal analysis methods to the general case on 585 massive datasets. A separate parallel calculator was created to help our team 586 keep track of their work for ensuring its continuous improvement, which was then 587 compared to an existing one to track of the same purpose, real crime data anal- 588 ysis was used as well as established tools to help in understanding how scalable 589 it is, and an assessment of performance gains occurred in this latter function. 590 Both the author highlighted Crimes, by their nature, are spatial phenomena, 591 and in terms of its locations, are temporal phenomena repetitive, descriptive, 592 describing, and objectively measuring the locations and time intervals of crim- 593 inal activity are used to classify, characterize, and document spatial-temporal 594 trends of criminal activity. Similar speech about criminal behavior improves 595 comprehension of commutability as well as spatial and temporal relationships. 596 21 This article explored this as something that is well suited to temporal stud- 597 ies because it provides spatially referenced crime data, or spatial correlations, 598 enabling crime data to be mapped out in time and determining the actual rela- 599 tionship between them. 600 601 2.3. Crime Hotspot Prediction 602 Yang et al. [28] delve into a large percentage of people, such as the general 603 public, are a ected by crime in a negative way. Previously, hotspot prediction 604 has been proposed as a means to expand law enforcement jurisdiction. There 605 are di erent ways of predicting which metropolitan regions are crime magnets, 606 some of which depend on data analysis of historical incidents and events, and 607 others which don't. It is only available through the current methods, though, 608 that have shown to have the ability to classify hotspots based on the past his- 609 tory of crime. None, however, have shown the predictive potential of the more 610 recent analyses of social and urban evidence. The author and group presented 611 this study in which we outline Crime Telescope, a visual platform combining 612 various kinds of data which employs both prediction and visualization to ex- 613 amine crime hotspots If the initial set of data is obtained, it searches for core 614 properties in the data through mathematical and linguistic study. Lastly, it 615 gathers data from various places and allows users to zoom in on them on a 616 digital map to nd speci c locations with highlighted risk areas. The paper 617 prospected that urban councils ought to do not only to enhance the protection 618 of city neighborhoods but also to help the environment of the towns and cities 619 where they live in. They turned inside out that a picture used to nd locations 620 with a signi cant rise in crime incidents, and then a dot or line indicates an 621 area's relationship to other dots in the chart and their general location on the 622 image, as criminologists use it in this context (i.e., crime hotspots). If police 623 and city governments want to nd out where the violence occurs in cities, they 624 need to use a high-predictive "hot spots" chart that more closely matches actual 625 areas where crime already occurs with risk factors. 626 22 627 Chainey [29] signi ed that a hotspot mapping strategy is also employed for 628 assisting police with enforcement activities including directing patrols to cer- 629 tain areas where they are needed and lowering crime. Histogram approximation 630 is still has long been used, but in modern times there are several alternative 631 methods that have emerged, which seem to be more common is KDE. Known 632 because of the unique graphical representation of crime visualization and is the 633 most closely followed by government crime analysts, which uses the density of 634 land mapping is that KDE also incorporates is considered with other hotspot 635 mapping techniques. Entering values for two key parameters in the command 636 produces two kinds of output: cell size and bandwidth must be done to achieve 637 KDE results. The few studies that have been done have looked at these to 638 date on their in uences have so far have only looked into their general hotspot- 639 mapping purposes. No study has yet looked at the function of these criteria on a 640 hotspot chart, although it can be assumed that these may be used to see where 641 crime occurs in the future. This studies helped to ll this void by utilizing infor- 642 mation on various cell sizes and statistics on crimes committed by people with 643 separate from the population on residential robbery and abuse. This study es- 644 tablished that a null in uence relationship between cell size and crime hotspots 645 on spatial trends of crime, but wireless capacity does impact crime. In the 646 end, we will be addressing how the ndings of this research can enable the po- 647 lice and researchers to improve the way we use KDE for criminal investigation. 648 The author explored that Not only has the introduction of heat mapping, but 649 even the use of hot-spot policing is credited with the birth of this new approach. 650 651 Araujo et al. [30] highlighted the use of an algorithmically an infrastruc- 652 ture architecture, which can then be implemented to service current and future 653 public protection networks and channels. The system is based on the ROTA 654 strategy, with public safety initiatives as its background, which was devised 655 for the greater purpose of improving urban services in Brazil's new African 656 metropolis, Natal. The results of an experiment gave valuable information re- 657 23 garding the various estimators. Furthermore, the outcomes of the algorithm 658 selection test on the hotspot detection challenge shown by executing a simu- 659 lation. The author suggested a statistical method for detecting hotspots that 660 addresses geographical, temporal, and categorical con gurations of crime utiliz- 661 ing machine learning algorithms that are more speci cally calibrated for average 662 danger levels. This architecture is applied as a service using the algorithm as 663 an API. The structure is able to accommodate many data streams, has several 664 di erent ways of seeing, o ers a wide range of speci cations, which allows for 665 machine learning to be included, and includes the use of an algorithmic decision 666 making. The system produces an ecient artifact for the process of developing 667 and maintaining patrols, called a program card. Author and the group explored 668 that a signi cant amount of research has shown that the possibility of predictive 669 analyses leading to higher accuracy in identifying patterns and predictions of 670 crime patterns has been explored through using quantitative experiments. The 671 use of a novel portfolio of machine learning algorithms o ers a mechanism that 672 more speci cally identi es locations and periods when law enforcement ocers 673 might be at greater risk of being targeted, allowing them to adjust their sched- 674 ules to certain instances with more exactitude. 675 676 Kang & Kang [31] signi ed to increase the accuracy of criminal activity 677 modeling; they investigated the design of protective factors, for example, the 678 principle of broken windows and the use of environmental protection measures. 679 To present this article, they have come up with a data fusion approach that 680 takes the text and contextual information into account, which utilizes a deep 681 neural network (DNN). They did a comparative analysis of the interactions to 682 see whether crime occurrences were correlated with the results of the study. This 683 process allowed for an excellent and successful forecast of crime patterns to be 684 made, as deviants harm the prediction. It is a multi-modal prediction model 685 and suggested here that they shall use to make more reliable estimates of crimi- 686 nal activities. The dataset of this article was compiled from numerous Chicago, 687 Illinois web datasets, such as violence, demographic, and meteorological and 688 24 photographic/archaeological information, as well as photos and photographs of 689 the city. First, Authors did some statistical analysis on crime-making patterns 690 before training the model with crime-related evidence. With spatial, tempo- 691 ral, environmental, and function dependencies, our DNN additionally consists 692 of four layers: spatial, temporal, environmental, meaning, and joint feature 693 representation. Coupled with the pertinent data from di erent contexts, our 694 analytics is an ecient decision-making process that employs data redundancy 695 analysis, our DNN integration is an e ective way to glean critical insights. 696 697 Macbeth & Ariel [32] explored that the rst phase of a proactive hot spots 698 policing approach was the e ective detection of crimes that have been occurring 699 for a long time and can be expected to continue. The second stage is man- 700 aging any crime that is changing or appearing for the rst time; such issues 701 cannot be de ned as hot spots, since they are still volatile and inconsistent. 702 Based on research this document indicated where violence is likely to occur, 703 "recidive hot zones" is observed to focus more speci cally. when using this lo- 704 cation feature, statistical forecasts will serve as a kind of professional judgment 705 as a "experts" about whether an idea has promise, through how well it would do 706 in various places. Although well-positioned predictive \hot spots" and \hurri- 707 cane damage" forecast possible events of coastal ooding, just 50% of the paths 708 are marked correctly" Business and political judgment will erroneously locates 709 future crimes, and is more suitable for handling incidents of a criminal nature. 710 Recidivism is a signi cant, however only one is relevant since it is characterized 711 as a \a hot spot on the map": it is the precise region in microdot locations. 712 713 Lin et al. [33] enlightened that grid-based crime modeling and spatio- 714 temporal details provided by the Google's Places API was used to show a va- 715 riety of temporal and spatial characteristics in Taoyuan City, Taiwan. In the 716 rst place, the authors have examined two traditional approaches: a spatial- 717 temporal and an empirical model. The use of spatial and temporal models is 718 widely accepted as being e ective for crime reduction, including Kernel Density 719 25 (KD) and Time Series. They expanded on sentence: These two approaches only 720 deal with time or space only, but crime is signi cantly in uenced by a lot of 721 other variables. Due to the centralized data processing and grid-based reason- 722 ing, machine learning is particularly useful for grid-based crime prediction. The 723 most accurate model was learned to be a new Deep Neural Networks classi er, 724 which was found to outperform Support Vector Machines, Random Decision 725 Forests, and K-Nearest Neighbor algorithms. By comparing our F1 scores to 726 a base scores, the design' 100-baseline 100-month moving average, they found 727 that our performance increased by around 7% (meaning its top score improved). 728 This article proved that their design outperforms the standard by being more 729 e ective at locating crime displacement in the designs. In this case, they de- 730 scribed "generally" as the process of creating new algorithms, or "typically" of 731 improving feature design model eciency to raise model accuracy,whereas ker- 732 nel density estimation (high pattern-level estimation) models which often look 733 for blocks and distributions (predictions resulting from) diminishing data. 734 735 The extensive literature survey performed on diverse techniques of spatial in- 736 terpolation and autocorrelation techniques present that no convergence has been 737 observed till now. This paper provides a proposed framework which presents a 738 novelty in the domain of GIS which shall have immense applicability in diverse 739 domains. It can be observed that Kriging interpolation and kernel density in- 740 terpolation are widely used interpolation techniques in GIS. Further, Getis-Ord 741 G ialso is a popularly used tool in the domain of crime analysis since it provides 742 hotspots and coldspots in a very ecient manner. 743 3. Materials and Methods 744 3.1. Proposed Framework 745 The proposed framework is a four-tiered architecture represented in Figure 746 1 which epitomizes the implementation of spatial interpolation techniques con- 747 verged with spatial autocorrelation features. The primary tier i.e., the geospatial 748 26 data pre-processing tier serves the most inevitable part of the implementation 749 framework. Since, the data available have multiple lacunae such as missing val- 750 ues, noise and many such. To deal with such data we need to pre-process the 751 dataset. Since, we are dealing with spatial interpolation as well as autocorrela- 752 tion, we require the creation of vector les i.e., in ".shp" format (also called as 753 shape les). Further, missing values can be handled by either properly lling 754 the values or by instantiating the values with a common name like N/A, Null, 755 etc. Further, in cases where we are required to use non-geospatial data i.e., 756 data with no coordinate speci cations, we need to perform geo-coding opera- 757 tion where automatically the dataset is lled with latitude and longitude values 758 which makes the dataset geospatially compatible. The second tier comprises of 759 the vector analysis tier where initially we need to join a common attribute of 760 the vector le and the considered dataset in order to make the vector le suit- 761 able for further analysis. Then, we need to generate choropleth map in order to 762 classify diverse zones based on the speci c classi able attribute. A graduated 763 color map is generated where we are required to x the range of the classi able 764 attribute based on which color allocation will take place. After the choropleth 765 with proper rane xation is done, the next layer needs to be implemented. 766 767 The interpolation tier deals with diverse interpolation methods. We need 768 to specify radius at which interpolation shall be carried out. Further, shape 769 of interpolation also needs to be speci ed based on the application domain. 770 For, TIN interpolation there exists two interpolation attributes i.e, linear and 771 Clough-Toucher (cubic). Further, for IDW interpolation we require distance at- 772 tribute only. Also, for kernel density interpolation we need to specify the kernel 773 shape i.e., either quartic (bi-weight), triangular, tri-weight, uniform or Epanech- 774 nikov. After the interpolation is successfully implemented over the vector le, 775 we need to perform autocorrelation which is epitomized in the subsequent tier. 776 777 The fourth tier deals with spatial autocorrelation implementation. We need 778 to specify the LISA indicator which stands for local indicators of spatial asso- 779 27 ciation. There are two types of LISA indicators existing in GIS i.e., Moran's I 780 and Getis-Ord G i. Moran's I is used in case of identifying correlation among 781 two data points whereas Getis-Ord G iis used to nd hotspots i.e., highly dense 782 regions as well as coldspots i.e, lowest dense regions. In order to perform the 783 task of autocorrelation we need to specify the spatial weight matrix which are 784 of types : queen, rook, bishop, distance and nearest neighbors. After, the au- 785 tocorrelation is performed we observe a graduated map providing pvalues as 786 well as zscores. 787 The output is a weighted-overlay map, heatmap, choropleth map classi ed 788 according to the considered attribute, an interpolated map, spatial autocorre- 789 lation map providing proper zonal analysis and corresponding pvalues and 790 zscores. 791 3.2. Dataset Speci cation 792 The data collected from [34] comprised of 15 columns and 2,829 rows i.e., a 793 total of 42,435 instances which comprised crime statistics from 1975 till 2015. 794 We have reduced the number of instances as per our implementation conve- 795 nience. The modi ed dataset comprised of 16 columns and 414 rows i.e., a total 796 of 6,624 instances that comprised of crime statistics from 2010 till 2015. Also, 797 the base data didn't have latitude and longitude values and thus, the modi ed 798 data has been geocoded to automatically allocate latitude and longitude values 799 to each row. Also, since it becomes dicult to join the dataset with the vector 800 shape le if joining attribute is not properly designed, this led towards adding 801 one more column to the modi ed dataset that entailed provinces to which the 802 regions belong to. Thus, the nal dataset was made geospatially compatible in 803 order to perform geospatial crime analytics over it. The prime attributes i.e., 804 selected during the implementation phase includes number of violent crimes, 805 latitude, longitude, crime per capita, and region. The term "crime per capita" 806 means the ratio of number of violent crimes to the total population in a region- 807 wise fashion. 808 28 Figure 1: 4-tiered proposed framework for geospatial analytical implementation in context of spatial interpolation and autocorrelation. 3.3. Experimental Setup 809 The machine on which the experimentation has been performed includes 810 hardware speci cations as 1 TB hard disk drive (HDD), 4 GB random access 811 memory (RAM), Intel Core i3 processor, and 2GB graphics card. The software 812 29 tools used for the desired implementation is QGIS 3.12 Bucuresti. For dataset 813 modi cation, the tool used is Microsoft Excel. Also, in order to perform the 814 geocoding operation on the dataset, Google Sheets were used with GeoCode 815 package so as to allocate geographical coordinates to each region automatically 816 by retrieving it from Google. 817 4. Results and Discussion 818 This chapter entails all the results obtained by practically realizing the pro- 819 posed framework. Figure 2 presents the initial task of geospatial crime ana- 820 lytics i.e., weighted-overlay analysis with labels signifying regions in di enent 821 provinces of U.S.A. where crime has been reported as per the considered dataset. 822 Further, to visualize crime per capita in a region-wise fashion, a weighted-overlay 823 map has been labelled with the crime statistics in Figure 3. Further, to identify 824 crime hotspots based on crime per capita parameter, a symbological graduated 825 heatmap has been designed where based on color intensity the density of crime 826 can be visualized in diverse regions of U.S.A. The heatmap is presented in Fig- 827 ure 4. Further, a vector analysis, i.e., a choropleth map is designed after joining 828 the vector layer with the dataset and properly labelling it in equal intervals is 829 presented in Figure 5. Finally, the prime motto of geospatial crime analytics 830 is ful lled by performing interpolation implementation with the assistance of 831 kernel density estimation. The radius is set to 5 units and kernel function set 832 to "quartic", an interpolated map is obtained which is represented in Figure 6. 833 Further, Getis-Ord G iwhich is a local indicator of spatial association, used to 834 signify spatial autocorrelation is implemented. Based on the zscores obtained, 835 the map is autocorrelated spatially. The regions can be identi ed as hotspot or 836 coldspot based on the color code allocated as per speci c zscores. The spatial 837 autocorrelation analysis is presented in Figure 7. 838 839 The results presented shows a detailed analysis of crime statistics in diverse 840 regions of U.S.A. based on which police patrolling, and other preventive mea- 841 30 sures can be taken in the regions which are identi ed as crime hotspots. Further, 842 the results also can be visualized as a zonal analysis of crime parameters in the 843 regions of U.S.A. based on which risk analysis can also be done. 844 Figure 2: Weighted-Overlay analysis labelled with regions of crime in USA. 31 Figure 3: Weighted-Overlay analysis representing crime per capita in a region-wise fashion over USA vector le. 5. Challenges 845 There are few challenges which has been encountered during the phase of 846 implementation which includes: 847 848 32 Figure 4: Symobological graduated heatmap analysis to identify crime hotspots in USA based on historical data. •Dealing with geospatial big data is still a major challenge with local en- 849 gines which makes the geospatial big data analytics task a hectic one. 850 •Switching to cloud GIS for real-time implementation requires much re- 851 source to be allocated and its equivalent cost constraints. This challenge 852 33 Figure 5: Choropleth analysis performed by graduating regions based on crime statistics. can be overcame with serverless computing paradigm. 853 •Many machine learning tasks are used which are traditional like K-Means 854 clustering, DBSCAN clustering, and many such which reduces the pre- 855 dictive competency of the geospatial model. It can be tackled with the 856 34 Figure 6: Kernel density estimation performed with radius 5 units and quartic functionality to visualize spatial interpolation. advent of deep learning models. 857 •Crime hotspots could be predicted through this dissertation but to predict 858 where next crime is going to happen requires much more powerful model 859 since crime is an unprecedented event. 860 35 Figure 7: Getis-Ord G itechnique of spatial autocorrelation implemented to identify crime hotspots and coldspots. 6. Conclusion and Future Scope 861 The report consisted a thorough literature survey on spatial interpolation 862 techniques as well as spatial autocorrelation methods used in the eld of geospa- 863 tial crime analytics. The report also entailed a proposed framework which epit- 864 36 omized the implementation of the merger of spatial interpolation and auto- 865 correlation for a better geospatial crime analysis paradigm. The interpolation 866 techniques entailed in this report includes Kringing interpolation, TIN interpo- 867 lation, IDW interpolation and kernel desity interpolation. Further, the two spa- 868 tial autocorrelation techniques i.e., also known as LISA indicators were enclosed 869 within this report that comprised of Moran's I and Getis-Ord G itechniques. 870 In order to practically realize the ecacy of the proposed framework to perform 871 geospatial crime analytics, a dataset comprising of diverse crime types enclosed 872 within the attribute "violent crimes" for the regions in U.S.A. has been consid- 873 ered. Further, the dataset has been geocoded to automatically allocate latitude 874 and longitude values to each region. The dataset after preparation has been 875 overlaid on the vector shape le of U.S.A. so as to perform the weighted-overlay 876 analysis. The weighted-overlay analysis maps have been labelled with di erent 877 key attributes. Then, heatmap analysis has been performed and crime hotspots 878 has been identi ed based on crime statistics. Further, the choropleth analysis 879 has been performed in order to epitomize the join operation between the vec- 880 tor shape le and the corresponding dataset. Also, the interpolation technique 881 chosen from the proposed framework was kernel density interpolation. Kernel 882 density interpolation by specifying kernel function to be "quartic" has been im- 883 plemented to visualize the interpolated map. Finally, spatial autocorrelation 884 was implemented using the immensely used technique in the domain of crime 885 analytics i.e., Getis-Ord G i. The considered interpolation technique is well- 886 known since it can classify both hotspots as well as coldspots (crime hotspots 887 and coldspots in our case). Thus, a detailed geospatial crime analytics has been 888 performed in this dissertation to entail all the available GIS-based features which 889 can be used to eciently perform geospatial crime analytics. The emerging rage 890 of GIS technology has led towards immense applicability areas where GIS can 891 play a key role. Sectors like crime mapping, disaster management, healthcare 892 and many such have immense applicability of pro cient techniques. Hotspot 893 identi cation is a prime task in GIS and it can be done with the assistance of 894 spatial interpolation by generating heatmaps based on density as well as spatial 895 37 autocorrelation techniques. The ever-growing demand of GIS technology can 896 have future spaces where the task of interpolation and autocorrelation might be 897 merged with techniques of arti cial intelligence, machine learning, cloud com- 898 puting paradigm, serverless computing framework and many such. 899 900